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Record W4312059830 · doi:10.1002/qre.3247

Progressive system safety and reliability analysis: A sustainable game theory approach

2022· article· en· W4312059830 on OpenAlex
Mohammad Yazdi, Yiyuan Ding, Sidum Adumene, Parastoo Shafie

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuality and Reliability Engineering International · 2022
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsReliability (semiconductor)Game theoryComputer scienceCooperative game theoryReliability theoryDecision theoryManagement scienceOperations researchSequential gameRisk analysis (engineering)Reliability engineeringEngineeringMicroeconomicsEconomicsBusiness

Abstract

fetched live from OpenAlex

Abstract This research aims to study the applicability of game theory to system safety and reliability decision‐making problems and corresponding objective conflicts using non‐cooperative games. The non‐cooperative games would solve the games considering non‐cooperative cognitive decision‐makers behaviors, which are commonly ignored by other system safety and reliability analysis (SSRA) techniques, assuming that there would be perfect cooperation between the players (decision‐makers). Game theory can also recognize and understand the decision‐makers' behaviors and provide a “win‐win” situation for all players and the best broader system outcomes. The paper also shows the use of dynamic game theory in system safety and reliability decision‐making problems over time. The results indicate the effectiveness and efficiency of game theory and show how this can better reflect decision‐makers’ opinions in system safety and reliability decision‐making problems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.227
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it